Heart Attack Analysis Introduction A heart attack occurs when an artery supplying your heart with blood and oxygen becomes blocked. A blood clot can form and block your arteries, causing a heart attack. This Heart Attack Analysis helps to understand the chance of attack occurrence in persons based on varied health conditions.
Dataset The dataset is Heart_Attack_Analysis_Data.csv. This dataset contains data about some hundreds of patients mentioning Age, Sex, Exercise Include Angina(1=YES, 0=NO), CP_Type (Chest Pain)(Value 1: typical angina, Value2: atypical angina, Value 3: non-anginal pain, Value 4: asymptomatic), ECG Results, Blood Pressure, Cholesterol, Blood Sugar, Family History (Number of persons affected in the family), Maximum Heart Rate, Target -0=LESS CHANCE , 1= MORE CHANCE
Aim
• Building a Predictive Model using Naïve Bayesian Approach (Which features decide heart attack?)
• Commenting on the performance of this model using AUC-ROC, Precision, Recall, F_score, Accuracy
The ML pipeline includes the following steps:
a) Preprocessing the data to enhance quality
b) Carrying out descriptive summarization of data and make observations
c) Identifying relevant, irrelevant attributes for building model.
d) Using data visualization tools and make observations
e) Carrying out the chosen analytic task. Show results including intermediate results, as needed
f) Evaluating the solution
This project is made using Jupyter Notebook
a) Implemented predictive models/classifiers using the following classification approaches: • Logistic Regression • Decision tree • Ensemble Methods (e.g., Random Forest) • K-Nearest Neighbour
b) Compared the performances of each model/classifier considering the given dataset using different evaluation measures such as Precision, Recall, F1-Score, AUC-ROC. Showed the comparison chart in Python notebook
c) Identified the model which was the best amongst all the models you have built with explaination.